A postoperative nursing management system and method for neurosurgery

Abstract

This invention discloses a postoperative nursing management system and method for neurosurgery. Belonging to the field of medical informatics and intelligent monitoring-assisted decision-making technology, the system acquires multimodal vital sign data streams of target subjects in real time, constructs a time series by setting dynamically sliding observation windows on a continuous time axis, extracts the changing trend features of vital sign indicators in each dimension and performs joint correlation discrimination, generating an early warning trigger signal when the combined state meets preset high-risk conditions; subsequently, it extracts the current multimodal monitoring features and performs feature similarity matching with structured scenario models in a preset emergency plan library to filter target scenario models; finally, it automatically distributes standardized treatment instructions and generates a traceability log based on the model. This invention effectively overcomes the time lag and high false alarm rate defects of traditional single-indicator absolute threshold alarms, realizing early warning of insidious disease deterioration and fully automatic closed-loop distribution of emergency plans.

Description

Technical Field

[0001] This application relates to the field of medical information technology and intelligent monitoring and decision support technology, and in particular to a neurosurgical postoperative nursing management system and method. Background Technology

[0002] In current clinical practice, the condition of critically ill patients after surgery, such as those in neurosurgery, often changes extremely rapidly, leaving healthcare professionals with a very narrow window for early diagnosis and intervention. Traditional postoperative care often relies on improvements to physical nursing devices or single-shot risk prediction models based on static perioperative snapshot data. These conventional methods generally lack the ability to dynamically perceive and track continuous, high-frequency physiological data streams, making it difficult to cope with acute pathological evolution measured in minutes or even seconds.

[0003] Furthermore, existing clinical monitoring and decision support systems generally suffer from severe isolation and rigidity in their early warning triggering mechanisms. Current systems typically set fixed absolute alarm thresholds for only a single monitoring device or a single vital sign indicator. This mechanism not only suffers from significant time lag, failing to effectively identify hidden risks, but also lacks the underlying logic for cross-modal time-series correlation calculations of multi-dimensional vital sign indicators. Consequently, in real-world clinical settings, these systems are highly prone to generating numerous false positives or failing to report complex combinations of pathological deterioration states.

[0004] With the deepening application of artificial intelligence and big data processing technologies in the medical field, improving the dynamic insight and rapid response capabilities of systems has become a core requirement. However, there is currently a lack of a suitable technology in this field that can effectively solve these problems.

[0005] The information disclosed in this background section is intended only to enhance the understanding of the overall background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this application is to provide a neurosurgical postoperative care management system and method to solve the technical problems in the prior art, such as the over-reliance on static single-time risk prediction for postoperative monitoring and the high false positive rate caused by the alarm time lag and single-index absolute threshold alarm.

[0007] The technical solution of the present invention is as follows:

[0008] On one hand, the present invention provides a neurosurgical postoperative care management system, including at least one processor and a memory communicatively connected to the processor. The memory stores computer program instructions, and when the processor executes the instructions, it implements the following module functions: a data acquisition and time series construction module, used to acquire multimodal vital sign data streams of target objects in real time, and set dynamically sliding observation windows on a continuous time axis to extract and construct a subset of time series data within the observation window; and a multidimensional correlation and early warning assessment module, used to extract the changing trend characteristics of vital sign indicators in each dimension within the time series data subset, and to assess multiple independent dimensions. The change trend features of the degree are subjected to joint correlation discrimination; when the combined state of the change trend features of each dimension indicator meets the preset high-risk conditions, an early warning trigger signal is generated; the scenario matching and decision module is used to respond to the early warning trigger signal, extract the current multimodal monitoring features of the target object, and perform feature similarity matching with multiple structured scenario models contained in the preset emergency plan library to select the most matching target scenario model; the response execution and closed-loop tracking module is used to automatically distribute the corresponding standardized disposal instructions according to the target scenario model, and record the time nodes and interaction operation details of the entire response execution process to generate a traceability log.

[0009] Optionally, the data acquisition and time series construction module is further configured to: receive raw data streams continuously transmitted from multiple medical monitoring devices, including at least intracranial pressure, blood pressure, heart rate, and blood oxygen saturation; denoise the raw data streams using a smoothing filtering algorithm; and complete the missing data frames using an interpolation algorithm to generate the cleaned time series data subset.

[0010] Optionally, when extracting the trend characteristics, the multidimensional correlation and early warning assessment module is specifically configured to: continuously update the time series data subset within the observation window and assess the trend of a single vital sign indicator over time; continuously compare the current absolute value of the vital sign indicator with a preset safe absolute threshold; if the assessment concludes that the trend characteristics represent a continuous deterioration trend, and the safe absolute threshold judgment mechanism is bypassed directly, the early warning trigger signal is generated in advance, provided that the current absolute value does not exceed the safe absolute threshold.

[0011] Optionally, the preset high-risk condition specifically includes a combined state association determination: when the target object's blood pressure index shows a downward trend, heart rate index shows an accelerating trend, and intracranial pressure index shows an increasing trend, it is determined that the combined state high-risk condition is met, and the warning trigger signal is triggered.

[0012] Optionally, in the emergency plan library called by the scenario matching and decision-making module, each structured scenario model is structurally encapsulated, and its internal feature structure includes at least: high-risk postoperative time window constraints corresponding to specific complications; characteristic ranges of typical changes in multiple vital signs; ranges of imaging feature calibration parameters or abnormal laboratory test indicators; and a standardized treatment instruction set strongly bound to the specific complication.

[0013] Optionally, when selecting a target scenario model, the scenario matching and decision-making module is specifically configured to: obtain the current vital signs change trend extraction results, imaging parameters, abnormal test indicators, and the time difference parameter between the current time and the end of the surgery for the target object, and assemble them into multidimensional current multimodal monitoring features; compare the current multimodal monitoring features with the standard feature parameters contained in each structured scenario model in a multidimensional space similarity comparison; select the structured scenario model with the highest feature overlap and that meets the system's preset matching tolerance threshold, and lock it as the target scenario model.

[0014] Optionally, the response execution and closed-loop tracking module is specifically configured to: convert the standardized treatment instructions into interactive graphical user interface cards and push them to the designated medical terminal; monitor the receiving, viewing and execution confirmation operations on the terminal, and capture the timestamp of each of the above interactive operations using a high-precision clock; encrypt the interactive records with additional timestamps and the physiological data stream segments before and after triggering the warning, and store them in the medical quality analysis database to form an immutable traceability log.

[0015] On the other hand, the present invention also provides a method for postoperative nursing management in neurosurgery, executed by a computing device, comprising the following steps: acquiring multimodal vital sign data streams of a target object in real time, and setting a dynamically sliding observation window period on a continuous time axis to extract and construct a subset of time-series data within the observation window period; extracting the trend features of changes in vital sign indicators of each dimension within the subset of time-series data, and performing joint correlation discrimination on the trend features of changes in multiple independent dimensions; generating an early warning trigger signal when the combined state of the trend features of changes in each dimension of indicators is determined to meet a preset high-risk condition; in response to the early warning trigger signal, extracting the current multimodal monitoring features of the target object, and performing feature similarity matching with multiple structured scenario models contained in a preset emergency plan library to select the most matching target scenario model; automatically distributing corresponding standardized treatment instructions according to the target scenario model, and recording the time nodes and interactive operation details of the entire response execution process to generate a traceability log.

[0016] Optionally, the steps of extracting trend features and generating early warning trigger signals specifically include: smoothing and denoising the multimodal vital sign data stream and interpolating to generate a cleaned subset of time series data; continuously comparing the current absolute values ​​of the vital sign indicators with a preset safety absolute threshold; if the current absolute value does not exceed the safety absolute threshold, and the trend features are assessed as a continuous deterioration trend, then the early warning trigger signal is generated ahead of time, bypassing the safety absolute threshold judgment mechanism; and when the joint correlation judgment simultaneously determines that the target object's blood pressure indicator shows a decreasing trend, heart rate indicator shows an increasing trend, and intracranial pressure indicator shows an increasing trend, it is established as meeting the high-risk condition of the combined state.

[0017] Optionally, the steps of selecting the most matching target scenario model and generating a traceability log specifically include: obtaining the current vital sign change trend extraction results, imaging parameters, abnormal test indicators, and the time difference parameter between the current time and the end of the surgery for the target object, and assembling them into multidimensional current multimodal monitoring features; comparing the current multimodal monitoring features with the standard feature parameters contained in each structured scenario model in a multidimensional space similarity comparison, and selecting the structured scenario model with the highest feature overlap and meeting the preset matching tolerance threshold as the target scenario model; converting the standardized treatment instructions into interactive graphical user interface cards and pushing them to the designated medical terminal; using a high-precision clock to capture timestamps for the receiving, viewing, and execution confirmation operations on the terminal, and encrypting and storing the interaction records with additional timestamps and physiological data stream fragments before and after triggering the warning to form an immutable traceability log.

[0018] The beneficial effects of this invention are as follows:

[0019] This invention, by setting a dynamically sliding observation window on a continuous time axis and extracting the changing trend characteristics of vital signs indicators in various dimensions within a subset of time-series data, enables the system to completely break free from the rigid triggering logic of traditional systems based on absolute numerical anomalies at a single time point. Because the system can continuously evaluate the dynamic evolution rate of indicators at the underlying level, it can keenly detect the hidden trend of continuous deterioration even when the absolute values ​​of the target's vital signs have not yet exceeded the set safety threshold. This trend-level proactive insight mechanism directly overcomes the severe time lag blind spot inherent in traditional absolute threshold alarms, thus successfully achieving early warning of pathological states and ultimately securing a golden intervention window for medical personnel to deal with life-threatening complications.

[0020] Meanwhile, this invention breaks through the technical bias of existing monitoring equipment that isolates and analyzes single physiological indicators by performing joint correlation discrimination on the changing trend characteristics of multiple independent dimensions and setting high-risk conditions for combined states. At the underlying logic level, this multi-dimensional cross-validation and joint discrimination mechanism can systematically depict deep-seated multi-system pathological linkage trajectories. Precisely because it abandons the one-sided reliance on single-dimensional data, the system can effectively filter and shield against instantaneous fluctuation noise of single indicators caused by changes in the target subject's position or accidental interference from the instrument, thereby significantly reducing the false positive rate in the clinical environment and alleviating the cognitive load and "alarm fatigue" of medical staff caused by invalid alarms.

[0021] Furthermore, this invention extracts the current multimodal monitoring features of the target object and performs feature similarity matching with multiple structured scenario models contained in a pre-set emergency plan library, constructing a seamless decision-making channel from bottom-level data anomaly perception to top-level clinical intervention execution. When a bottom-level warning is triggered, the system can automatically select the target scenario model with the highest feature overlap without relying on tedious human experience for etiological inference and plan review, and automatically distribute the corresponding standardized treatment instructions. This decision-response mechanism based on high-dimensional feature space mapping not only ensures the speed and standardization of emergency response in extremely critical situations, but also, through the automated collection and encrypted storage of time nodes and interactive operation details throughout the response execution process, directly generates an immutable traceability log, thus providing a rigorous and objective data closed-loop support for medical institutions' quality control, accountability, and continuous iteration of subsequent plan models. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of the logical architecture modules of a neurosurgical postoperative care management system provided in an embodiment of the present invention;

[0024] Figure 2 This is a schematic diagram of the overall operation steps of a neurosurgical postoperative nursing management method provided in an embodiment of the present invention;

[0025] Figure 3 This is a schematic diagram illustrating the logical principle of the dynamic sliding observation window period interception and trend feature extraction mechanism provided in this embodiment of the invention;

[0026] Figure 4This is a schematic diagram illustrating the process of the multidimensional vital sign indicator temporal feature joint correlation discrimination and early warning triggering mechanism provided in this embodiment of the invention;

[0027] Figure 5 This is a schematic diagram of the closed-loop process for multimodal monitoring feature space similarity matching and structured scenario model decision-making provided in this embodiment of the invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention; that is, the described embodiments are merely some embodiments of the invention, and not all embodiments. The components of the embodiments of the invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0029] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0030] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0031] As mentioned earlier, in current clinical medical practice, the condition of critically ill patients after surgery, such as those in neurosurgery, usually changes extremely rapidly. Existing clinical monitoring and decision support systems generally suffer from severe "isolation" and "rigidity" in their early warning triggering mechanisms. They typically set fixed absolute alarm thresholds for a single monitoring device or a single vital sign indicator, resulting in significant time lags. This easily leads to a large number of false positives or missed reports of complex combined pathological deterioration states, failing to ensure the timeliness and accuracy of medical intervention.

[0032] To address this issue, the present invention provides a neurosurgical postoperative nursing management system and method. By extracting time-series trend features of multimodal vital signs and performing multidimensional cross-discrimination, the system finally matches a structured scenario model in a high-dimensional feature space to issue decisions, thereby effectively suppressing the lag and false alarm problems of single absolute threshold alarms. The present invention solves this problem in the following way.

[0033] Example 1:

[0034] This embodiment provides a neurosurgical postoperative care management system, such as... Figure 1 , Figures 2 to 5 As shown, the physical topology of this system can be deployed within a local area network environment, connecting bedside devices in the intensive care unit with the hospital data center. The system includes at least one processor and a memory communicatively connected to the processor. The memory stores computer program instructions. When the processor executes the instructions, the system logically performs the following module functions:

[0035] The data acquisition and time-series construction module is used to acquire multimodal vital sign data streams of the target object in real time. Specifically, this module receives raw data streams continuously transmitted from various medical monitoring devices, including at least intracranial pressure, blood pressure, heart rate, and blood oxygen saturation. Due to the complex clinical environment, the raw data stream often contains artifact noise caused by changes in the target object's body position or flushing of tubing. To address this, the system uses a smoothing filtering algorithm to denoise the raw data stream and employs an interpolation algorithm to complete missing data frames, generating a cleaned, continuous data stream.

[0036] Subsequently, the system sets dynamically sliding observation windows on a continuous time axis to extract and construct a subset of time-series data within the observation window. Let the continuous time-series data stream of specific vital signs indicators received and cleaned by the system be denoted as set. The corresponding set of timestamp sequences is denoted as The system defines the time span of the sliding observation window as a constant. At any current physical moment The subset of data points included in the effective observation window of the current calculation period is defined as Let the total number of valid sampled data points contained in this subset be . .

[0037] The multidimensional correlation and early warning assessment module is used to extract the changing trend characteristics of vital sign indicators in each dimension within the time series data subset. The system uses a linear regression algorithm based on the least squares method to calculate the overall data changing trend characteristics (i.e., trend gradient) within the window period in real time. The calculation process of the trend gradient is expressed by the following formula:

[0038]

[0039] in,

[0040] To solve for the trend gradient of a specific dimension of vital signs within the observation window, we define the average rate of change of that indicator per unit time. The time indicates an overall upward trend, when The time indicates an overall downward trend; This represents the total number of valid sampled data points within the sliding observation window. For the first observation window A timestamp variable for each data point; To be at the corresponding time point The specific measurement values ​​of the vital signs obtained.

[0041] After extracting trend features, the module continuously compares the current absolute values ​​of the vital signs with preset safe absolute thresholds. If the current absolute value does not exceed the safe absolute threshold, and the assessment concludes... If the trend is characterized as a continuous deterioration (e.g., exceeding the set continuous rise judgment constant), the system will directly bypass the safety absolute threshold judgment mechanism and generate the early warning trigger signal in advance.

[0042] Furthermore, this module performs joint correlation discrimination on the changing trend features of multiple independent dimensions. The system calculates the trend gradient of each dimension within the observation window in parallel and constructs a joint risk index. The joint calculation process of the joint risk index is expressed by the following formula:

[0043]

[0044] in, For the system at the current moment The combined risk index is determined by the assessment. The higher the index value, the greater the probability of the combined risk occurring. The total number of dimensions of vital sign indicators participating in the joint computation; Assign the system to the first The weight coefficients for each dimension are forced to satisfy the normalization condition. ; In order to target the The trend gradient in each dimension The constructed normalized nonlinear activation function is specifically designed to capture and amplify significant upward or downward trend features of specific variables.

[0045] To address the challenge of inconsistent dimensions among different vital sign parameters, the normalized nonlinear activation function is specifically divided into positive trend activation functions based on the direction of pathological changes in physiological indicators. With negative trend activation function .

[0046] The positive trend activation function The calculation process is expressed by the following formula:

[0047]

[0048] in, This is the extreme risk rise slope constant of this single indicator, calculated based on a large sample of historical clinical monitoring data.

[0049] The negative trend activation function The calculation process is expressed by the following formula:

[0050]

[0051] in, This is the limit of the extreme negative slope constant when the system's judgment index experiences an acute decline.

[0052] Specifically, in response to systemic pathological responses (such as decreased blood pressure, increased heart rate, and increased intracranial pressure), the system constructs a specific combined risk index. Its calculation process is expressed by the following formula:

[0053]

[0054] in, For the extracted intracranial pressure trend gradient, For heart rate trend gradient, Blood pressure trend gradient; , , This is a pre-defined weight coefficient matrix.

[0055] When the combined state of the changing trend characteristics of the indicators in each dimension meets the preset high-risk conditions (for example, after Boolean logic and weighted operations, it is determined that the target object's blood pressure indicator shows a downward trend, the heart rate indicator shows an accelerating trend, and the intracranial pressure indicator shows an increasing trend, and the combined risk index...), If the risk threshold is greater than or equal to the total risk control threshold set by the system, it is established as a high-risk condition that meets the combined state, and an early warning trigger signal is generated.

[0056] The scenario matching and decision-making module is used to respond to the warning trigger signal and call a preset emergency plan library in the database. The emergency plan library stores multiple structured scenario models, each of which is structured and encapsulated. Its internal feature structure includes at least: high-risk postoperative time window constraints corresponding to a specific complication; characteristic ranges of typical changes in multiple vital signs; ranges of imaging feature calibration parameters or abnormal laboratory test indicators; and a standardized set of treatment instructions strongly bound to the specific complication.

[0057] The structured scenario model set in the emergency response plan library includes at least three typical high-risk scenario models: a postoperative early-stage acute intracranial hemorrhage scenario model with extremely high risk and a very short time window; a peak-stage cerebral edema scenario model with medium- to long-term persistence and relatively slow changes in vital signs; and a postoperative epileptic seizure scenario model with sudden onset, electrophysiological disturbances, and a cliff-like drop in blood oxygenation. Each of the structured scenario models is encapsulated as an independent digital container containing a multimodal high-dimensional vector template.

[0058] This module acquires the current vital signs change trends, imaging parameters, abnormal test indicators, and the time difference between the current time and the end of the surgery for the target object, and assembles them into a multidimensional current multimodal monitoring feature vector. Subsequently, the system calls the spatial distance calculation function to calculate the multidimensional spatial similarity between the current multimodal monitoring features and the standard feature parameters contained in each of the structured scenario models. The operation process of the spatial distance calculation function is expressed by the following formula:

[0059]

[0060] in, The current state feature vector finally calculated by the algorithm With the first in the contingency plan database Standard vector of a pre-defined scenario model The weighted, standardized Euclidean distance between the two. The smaller the distance value, the higher the degree of overlap in their features; This represents the total number of feature dimensions involved in the calculation. For the first The weight coefficients of each feature dimension are used to adjust the determining power of different features on the final matching result; The current state feature vector In the middle, corresponding to the first Real-time calculation and extraction of values ​​in each dimension; For the first The preset scenario model standard vector In the middle, corresponding to the first The baseline values ​​for each dimension; The first one is obtained by pre-training based on historical statistics. The global standard deviation of each feature dimension is used for dimensional normalization of the feature data.

[0061] The system iterates through and calculates all distance metrics, selects the structured scenario model with the smallest distance calculation result (i.e. the highest feature overlap) and meets the system's preset matching tolerance threshold, and locks it as the target scenario model.

[0062] The response execution and closed-loop tracking module automatically unpacks and distributes corresponding standardized treatment instructions based on the target scenario model. The system converts these standardized treatment instructions into interactive graphical user interface cards and pushes them to designated medical terminals. Simultaneously, the module's audit trail subroutine activates high-frequency clock monitoring, listening to the receiving, viewing, and execution confirmation operations on the terminals, capturing timestamps for each interaction using a high-precision clock. Finally, the system hashes and encrypts the timestamped interaction records and physiological data stream fragments before and after triggering the warning, storing them in a medical quality analysis database to form an immutable traceability log.

[0063] Example 2:

[0064] Based on the above embodiments, in order to provide a clearer and more complete explanation of the technical solutions therein, the present invention also provides Embodiment Two. This Embodiment Two, based on the system architecture and logical steps of Embodiment One, provides a completely new set of example data to calculate in detail the entire operation process of the neurosurgical postoperative nursing management system of the present invention in a real clinical scenario.

[0065] The execution calculations of the data acquisition and time series construction module:

[0066] The target patient was a patient who had returned to the intensive care unit after aneurysm clipping surgery. The system acquired the patient's multimodal vital signs data stream in real time and set a dynamically sliding observation window length constant. The sampling frequency is once every minute to extract valid feature values.

[0067] At the current physical moment The system extracts and cleans the total number of valid sampled data points within the current observation window. The corresponding set of timestamp sequences. Unit: minutes.

[0068] The specific measurement sequences of intracranial pressure (ICP), heart rate (HR), and systolic blood pressure (BP) acquired by the system during this period are as follows:

[0069] Intracranial pressure sequence (Unit: mmHg);

[0070] Heart rate sequence (Unit: bpm);

[0071] Blood pressure sequence (Unit: mmHg).

[0072] Operational calculations of the multidimensional correlation and early warning assessment module:

[0073] First, the system extracts the trend characteristics of vital sign indicators across various dimensions. Substituting the example data above into the trend gradient calculation formula in Example 1:

[0074]

[0075] Trend gradient of intracranial pressure (ICP) For example, in the calculation:

[0076] Known , ;

[0077] ;

[0078] ;

[0079] Substituting the data into the formula yields the following result:

[0080]

[0081] Similarly, the system calculates the heart rate trend gradient in parallel:

[0082] (bpm / min)

[0083] Blood pressure trend gradient:

[0084]

[0085] Although the current absolute value of intracranial pressure (18 mmHg) has not yet exceeded the clinically set safe absolute threshold (e.g., 20 mmHg), the system has successfully extracted... and The trend of significant deterioration.

[0086] Subsequently, the system substitutes the extracted trend gradient into a normalized nonlinear activation function. It is assumed that the system presets a constant for the slope of the intracranial pressure rise at extreme risks. Heart rate constant Acute drop in blood pressure threshold constant .

[0087] The activation value is calculated according to the formula in Example 1:

[0088]

[0089]

[0090]

[0091] Assume the system's weight coefficient matrix is ​​set as follows: , , Substitute the activation value into the formula for calculating the joint risk composite index:

[0092]

[0093] The conclusion is .

[0094] Since the calculated combined risk index of 0.60 is greater than the total risk control threshold set by the system (e.g., 0.50), the system determines that the combined state meets the high-risk condition and immediately generates an early warning trigger signal.

[0095] The operational calculations of the scenario matching and decision-making module:

[0096] In response to the early warning trigger signal, the system assembles the current multimodal monitoring features (including the aforementioned trend gradient) into a feature vector. To simplify the calculation, assume that the extracted core feature vector is... .

[0097] The system retrieves the "Postoperative Early-Stage Acute Intracranial Hemorrhage Scenario Model" from the emergency response plan database, and its standard feature vector baseline value is... .

[0098] Assuming weight coefficients And global standard deviation Substitute the data into the spatial distance calculation function:

[0099]

[0100] The calculation shows that:

[0101]

[0102] .

[0103] Since the calculated spatial distance metric value of 0.547 is less than the system's preset matching tolerance threshold (e.g., 1.0), the system determines that the feature overlap is extremely high, and locks the "postoperative early acute intracranial hemorrhage scenario model" as the target scenario model.

[0104] The system's response execution and closed-loop tracking module performs the following calculations: Based on the successfully matched target scenario model, the system automatically unpacks and outputs the corresponding standardized treatment instructions (e.g., pushing a GUI instruction card to the target nurse's terminal with the message "Immediately stop vasodilators, elevate the head of the bed, prepare for mannitol IV bolus"). The system's high-frequency clock synchronously captures the timestamp of the nurse clicking "Confirm Receipt" (e.g., ...). (seconds), along with the complete physiological data stream fragment before the warning was triggered, are packaged and hashed together to complete the generation and persistent storage of medical quality traceability logs.

[0105] Through the above complete example data calculations, it is verified that the system of the present invention can accurately quantify multidimensional trends and achieve early warning and fully automatic intervention during the concealment period when the absolute threshold is not broken, based on the underlying formula.

[0106] Example 3:

[0107] Based on a unified overall inventive concept, this invention also provides a method for postoperative nursing management in neurosurgery. Please refer to [link to relevant documentation]. Figure 2 This third embodiment provides a method for postoperative nursing management in neurosurgery. This method can be applied to the postoperative nursing management system for neurosurgery described in the foregoing embodiments and is executed by a computing device, such as... Figure 2 As shown, the method includes the following steps:

[0108] Step S101: Acquire multimodal vital signs data stream of the target object in real time, and set a dynamically sliding observation window period on a continuous time axis to extract and construct a subset of time series data within the observation window period.

[0109] Specifically, the computing device receives raw data streams continuously transmitted from multiple medical monitoring devices, including at least intracranial pressure, blood pressure, heart rate, and blood oxygen saturation. Further, the multimodal vital signs data stream is smoothed, denoised, and interpolated to generate a cleaned subset of the time-series data.

[0110] Step S102: Extract the trend features of vital signs indicators in each dimension within the time series data subset, and perform joint correlation discrimination on the trend features of multiple independent dimensions; when it is determined that the combined state of the trend features of each dimension indicator meets the preset high-risk conditions, generate an early warning trigger signal.

[0111] Specifically, the computing device continuously compares the current absolute value of the vital signs with a preset safety absolute threshold; if the current absolute value does not exceed the safety absolute threshold, and the assessment indicates that the trend of change is a continuous deterioration trend, then the warning trigger signal is generated in advance, bypassing the safety absolute threshold judgment mechanism.

[0112] As an optional implementation, when performing joint association discrimination, if it is found that the target object's blood pressure index shows a decreasing trend, heart rate index shows an increasing trend, and intracranial pressure index shows an increasing trend, the computing device establishes that the high-risk condition of the combined state is met.

[0113] Step S103: In response to the warning trigger signal, extract the current multimodal monitoring features of the target object and perform feature similarity matching with multiple structured scenario models contained in the preset emergency plan library to select the most matching target scenario model.

[0114] Specifically, the computing device acquires the current vital sign change trend extraction results, imaging parameters, abnormal test indicators, and the time difference between the current date and the end of the surgery for the target object, and assembles these parameters to construct a multidimensional current multimodal monitoring feature. Further, the current multimodal monitoring feature is compared with the standard feature parameters contained in each structured scenario model in a multidimensional space similarity comparison. The structured scenario model with the highest feature overlap and meeting the preset matching tolerance threshold is selected and locked as the target scenario model.

[0115] Step S104: Automatically distribute the corresponding standardized handling instructions according to the target scenario model, and record the time nodes and interaction details of the entire response execution process to generate a traceability log.

[0116] Specifically, the computing device converts the standardized treatment instructions contained in the locked target scenario model into interactive graphical user interface cards and pushes them to the designated medical terminal. Simultaneously, a high-precision clock captures timestamps for the receiving, viewing, and execution confirmation operations on the terminal, and encrypts and stores the interaction records with attached timestamps and physiological data stream fragments before and after triggering the warning, forming an immutable tracing log.

[0117] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described method embodiments can be referred to the corresponding process in the foregoing system embodiments, and will not be repeated here.

[0118] It should be understood that, in the embodiments of the present invention, "B corresponding to A" means that B is associated with A, and B can be determined based on A. However, it should also be understood that determining B based on A does not mean that B is determined solely based on A; B can also be determined based on A and / or other information.

[0119] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0120] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0121] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces, apparatuses, or units, or they may be electrical, mechanical, or other forms of connection.

[0122] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of the present invention, depending on actual needs.

[0123] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0124] From the above description of the embodiments, those skilled in the art will clearly understand that the present invention can be implemented in hardware, firmware, or a combination thereof. When implemented in software, the above-described functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any medium that facilitates the transmission of a computer program from one place to another. Storage media can be any available medium accessible to a computer. For example, but not limited to, computer-readable media can include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code having the form of instructions or data structures and accessible to a computer. Furthermore, any connection can suitably be a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of the medium. As used in this invention, disk and disc include compressed optical discs (CDs), laser discs, optical discs, digital versatile discs (DVDs), floppy disks, and Blu-ray discs, wherein disks typically magnetically copy data, while discs optically copy data using lasers. The combinations described above should also be included within the scope of protection for computer-readable media.

[0125] In summary, the above description is merely a preferred embodiment of the technical solution of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A neurosurgical postoperative care management system, characterized by, It includes at least one processor and a memory communicatively connected to the processor, the memory storing computer program instructions, which, when executed by the processor, implement the following module functions: The data acquisition and time series construction module is used to acquire multimodal vital sign data streams of target objects in real time, and to set dynamically sliding observation windows on a continuous time axis to extract and construct a subset of time series data within the observation window. The multidimensional correlation and early warning assessment module is used to extract the changing trend features of vital signs indicators in each dimension within the time series data subset, and to perform joint correlation discrimination on the changing trend features of multiple independent dimensions; when it is determined that the combined state of the changing trend features of each dimension indicator meets the preset high-risk conditions, an early warning trigger signal is generated. The scenario matching and decision-making module is used to respond to the early warning trigger signal, extract the current multimodal monitoring features of the target object, and perform feature similarity matching with multiple structured scenario models contained in the preset emergency plan library to select the most matching target scenario model. The response execution and closed-loop tracking module is used to automatically distribute corresponding standardized handling instructions according to the target scenario model, and record the time nodes and interaction details of the entire response execution process to generate a traceability log.

2. The neurosurgical post-operative care management system of claim 1, wherein, The data acquisition and time series construction module is also used for: The system receives raw data streams continuously transmitted from various medical monitoring devices, including at least intracranial pressure, blood pressure, heart rate, and blood oxygen saturation; it then uses a smoothing filtering algorithm to denoise the raw data streams and employs an interpolation algorithm to complete any missing data frames, thereby generating a cleaned subset of the time-series data.

3. The neurosurgical post-operative care management system of claim 1, wherein, The multidimensional correlation and early warning assessment module is specifically configured to extract the trend features as follows: The time-series data subset within the observation window is continuously updated, and the trend of individual vital signs over time is evaluated. Continuously compare the current absolute values ​​of the vital signs indicators with the preset absolute safety threshold; If the current absolute value does not exceed the safety absolute threshold, and the assessment indicates that the trend of change is characterized as a continuous deterioration trend, then the safety absolute threshold judgment mechanism is bypassed directly, and the early warning trigger signal is generated in advance.

4. The neurosurgical post-operative care management system of claim 1, wherein, The preset high-risk conditions specifically include the determination of combined state associations: When the target's blood pressure index shows a downward trend, heart rate index shows an accelerating trend, and intracranial pressure index shows an increasing trend, it is determined that the combined state meets the high-risk condition, and the warning trigger signal is triggered.

5. The neurosurgical post-operative care management system of claim 1, wherein, In the emergency response plan library invoked by the scenario matching and decision-making module, each structured scenario model is structurally encapsulated, and its internal feature structure includes at least: Postoperative time window constraints for specific complications at high risk; Typical range of trends in multiple vital signs; The range of abnormal parameters in imaging features or laboratory tests; and A standardized set of treatment instructions that is strongly tied to this specific complication.

6. The neurosurgical post-operative care management system of claim 1, wherein, The scenario matching and decision-making module is specifically configured as follows when selecting target scenario models: The current vital signs change trends of the target object, imaging parameters, abnormal test indicators, and time difference parameters from the end of the surgery are obtained and assembled into multidimensional current multimodal monitoring features. The current multimodal monitoring features are compared with the standard feature parameters contained in each of the structured scenario models in a multidimensional space similarity comparison. The structured scenario model with the highest feature overlap and that meets the system's preset matching tolerance threshold is selected and locked as the target scenario model.

7. The neurosurgical post-operative care management system of claim 1, wherein, The response execution and closed-loop tracking module is specifically configured as follows: The standardized treatment instructions are converted into interactive graphical user interface cards and pushed to the designated medical terminals. The system monitors the receiving, viewing, and execution confirmation operations on the terminal, and uses a high-precision clock to capture the timestamp of each of the above interactive operations. The interaction records with additional timestamps and the physiological data stream segments before and after triggering the warning are encrypted and stored in the medical quality analysis database to form an immutable traceability log.

8. A neurosurgical postoperative care management method characterized by, Performed by a computing device, including the following steps: Real-time acquisition of multimodal vital signs data stream of target objects, and setting dynamically sliding observation windows on a continuous time axis to extract and construct a subset of time series data within the observation window; Extract the changing trend features of vital signs indicators in each dimension within the time series data subset, and perform joint correlation discrimination on the changing trend features of multiple independent dimensions; when it is determined that the combined state of the changing trend features of each dimension indicator meets the preset high-risk conditions, generate an early warning trigger signal; In response to the warning trigger signal, the current multimodal monitoring features of the target object are extracted and matched with the feature similarity of multiple structured scenario models contained in the preset emergency plan library to select the most matching target scenario model. Based on the target scenario model, the corresponding standardized handling instructions are automatically distributed, and the time nodes and interaction details of the entire response execution process are recorded to generate a traceability log.

9. The neurosurgical post-operative care management method according to claim 8, characterized in that, The steps of extracting trend features and generating early warning trigger signals specifically include: The multimodal vital signs data stream is smoothed, denoised, and interpolated to generate a cleaned subset of the time series data. The current absolute value of the vital signs is continuously compared with the preset absolute safety threshold. If the current absolute value does not exceed the absolute safety threshold, and the trend of change is assessed as a continuous deterioration trend, the warning trigger signal is generated in advance, bypassing the absolute safety threshold judgment mechanism. Furthermore, when the joint association judgment simultaneously determines that the target object's blood pressure index shows a decreasing trend, heart rate index shows an increasing trend, and intracranial pressure index shows an increasing trend, it is established as meeting the high-risk condition of the combined state.

10. The neurosurgical post-operative care management method according to claim 8, characterized by, The steps of selecting the most matching target scenario model and generating source tracing logs specifically include: The current vital signs change trend extraction results, imaging parameters, abnormal test indicators, and time difference parameters from the end of the surgery of the target object are obtained and assembled into multidimensional current multimodal monitoring features; The current multimodal monitoring features are compared with the standard feature parameters contained in each structured scenario model in a multidimensional space. The structured scenario model with the highest feature overlap and that meets the preset matching tolerance threshold is selected as the target scenario model. The standardized treatment instructions are converted into interactive graphical user interface cards and pushed to the designated medical terminal; a high-precision clock is used to capture timestamps of the receiving, viewing and execution confirmation operations on the terminal, and the interaction records with additional timestamps and physiological data stream fragments before and after triggering the warning are encrypted and stored to form the tamper-proof traceability log.

Images